Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
169 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Estimating Residential Solar Potential Using Aerial Data (2306.13564v1)

Published 23 Jun 2023 in cs.CV and eess.IV

Abstract: Project Sunroof estimates the solar potential of residential buildings using high quality aerial data. That is, it estimates the potential solar energy (and associated financial savings) that can be captured by buildings if solar panels were to be installed on their roofs. Unfortunately its coverage is limited by the lack of high resolution digital surface map (DSM) data. We present a deep learning approach that bridges this gap by enhancing widely available low-resolution data, thereby dramatically increasing the coverage of Sunroof. We also present some ongoing efforts to potentially improve accuracy even further by replacing certain algorithmic components of the Sunroof processing pipeline with deep learning.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (16)
  1. H Cantzler. Random sample consensus (ransac). Institute for Perception, Action and Behaviour, Division of Informatics, University of Edinburgh, 3, 1981.
  2. Depth map prediction from a single image using a multi-scale deep network. Advances in neural information processing systems, 27, 2014.
  3. Ssap: Single-shot instance segmentation with affinity pyramid. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 642–651, 2019.
  4. Exact maximum a posteriori estimation for binary images. Journal of the Royal Statistical Society: Series B (Methodological), 51(2):271–279, 1989.
  5. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016.
  6. A generalized multi-task learning approach to stereo dsm filtering in urban areas. ISPRS Journal of Photogrammetry and Remote Sensing, 166:213–227, 2020.
  7. Im2elevation: Building height estimation from single-view aerial imagery. Remote Sensing, 12(17):2719, 2020.
  8. NREL. Validating the accuracy of sighten’s automated shading tool. URL https://www.nrel.gov/docs/fy18osti/71313.pdf.
  9. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pages 234–241. Springer, 2015.
  10. Imagenet large scale visual recognition challenge. International journal of computer vision, 115(3):211–252, 2015.
  11. Wind effect on pv module temperature: Analysis of different techniques for an accurate estimation. Energy Procedia, 40:77–86, 2013.
  12. The national solar radiation data base (nsrdb). Renewable and sustainable energy reviews, 89:51–60, 2018.
  13. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1874–1883, 2016.
  14. Continental-scale building detection from high resolution satellite imagery. arXiv preprint arXiv:2107.12283, 2021.
  15. A Sole and A Valanzano. Digital terrain modelling. Geographical Information Systems in Hydrology, pages 175–194, 1996.
  16. Scalable height field self-shadowing. In Computer Graphics Forum, volume 29, pages 723–731. Wiley Online Library, 2010.
Citations (1)

Summary

We haven't generated a summary for this paper yet.